An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach
This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal c...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6658516 |
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| author | Weiwei Hao Hongyan Gao Zongqing Liu |
| author_facet | Weiwei Hao Hongyan Gao Zongqing Liu |
| author_sort | Weiwei Hao |
| collection | DOAJ |
| description | This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage. |
| format | Article |
| id | doaj-art-bf56a89a8c7d4d43a18d114b0d2beaa2 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-bf56a89a8c7d4d43a18d114b0d2beaa22025-08-20T03:23:07ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66585166658516An Evaluation Study on Investment Efficiency: A Predictive Machine Learning ApproachWeiwei Hao0Hongyan Gao1Zongqing Liu2School of Economics and Management, Beijing Jiaotong University, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing, ChinaThis paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage.http://dx.doi.org/10.1155/2021/6658516 |
| spellingShingle | Weiwei Hao Hongyan Gao Zongqing Liu An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach Complexity |
| title | An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach |
| title_full | An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach |
| title_fullStr | An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach |
| title_full_unstemmed | An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach |
| title_short | An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach |
| title_sort | evaluation study on investment efficiency a predictive machine learning approach |
| url | http://dx.doi.org/10.1155/2021/6658516 |
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